This chapter explores the vital role of Python performance optimization in data science, highlighting tools like Numba and profiling techniques. It discusses the importance of identifying performance hotspots and balancing optimization with code readability. The chapter further delves into specific profiling tools and techniques for enhancing execution speed and memory management, particularly in numerical computations.
Python performance has come a long way in recent times. And it's often the data scientists, with their computational algorithms and large quantities of data, who care the most about this form of performance. It's great to have Stan Seibert back on the show to talk about Python's performance for data scientists. We cover a wide range of tools and techniques that will be valuable for many Python developers and data scientists.
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